
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	@property
	def   _lowerCamelCase							(   self						:int		)						->					List[str]:
								__UpperCamelCase      :  List[str]       =   1
								__UpperCamelCase      :  List[str]       =   3
								__UpperCamelCase      :  Any       =   (3_2, 3_2)

								__UpperCamelCase      :  List[Any]       =   floats_tensor((batch_size, num_channels) + sizes	,  rng=random.Random(0		)		).to(a		)
								return image







	@property
	def   _lowerCamelCase							(   self						:Any		)						->					Dict:
								torch.manual_seed(0		)
								__UpperCamelCase      :  Tuple       =   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
								return model







	@property
	def   _lowerCamelCase							(   self						:Tuple		)						->					Any:
								torch.manual_seed(0		)
								__UpperCamelCase      :  str       =   AutoencoderKL(
								    block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
								return model







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Tuple:
								torch.manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   RobertaSeriesConfig(
								    hidden_size=3_2	,  project_dim=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1E-0_5	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=5_0_0_6	,  )
								return RobertaSeriesModelWithTransformation(a		)







	@property
	def   _lowerCamelCase							(   self						:Tuple		)						->					List[str]:
								def extract(*a						:Union[str, Any]	,  **a						:Union[str, Any]		):




															class 					lowerCamelCase__				:




																'''simple docstring'''




																def __init__(   self						:Optional[Any]		)						->					int:
																							__UpperCamelCase      :  Any       =   torch.ones([0]		)







																def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:Dict		)						->					Tuple:
																							self.pixel_values.to(a		)
																							return self
															return Out()

								return extract







	def   _lowerCamelCase							(   self						:List[str]		)						->					Dict:
								__UpperCamelCase      :  Tuple       =   "cpu"  # ensure determinism for the device-dependent torch.Generator
								__UpperCamelCase      :  Dict       =   self.dummy_cond_unet
								__UpperCamelCase      :  List[str]       =   PNDMScheduler(skip_prk_steps=a		)
								__UpperCamelCase      :  int       =   self.dummy_vae
								__UpperCamelCase      :  List[Any]       =   self.dummy_text_encoder
								__UpperCamelCase      :  Optional[int]       =   XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"		)
								__UpperCamelCase      :  List[Any]       =   7_7

								__UpperCamelCase      :  Optional[Any]       =   self.dummy_image.to(a		)
								__UpperCamelCase      :  List[Any]       =   init_image / 2 + 0.5

								# make sure here that pndm scheduler skips prk
								__UpperCamelCase      :  List[Any]       =   AltDiffusionImgaImgPipeline(
								    unet=a	,  scheduler=a	,  vae=a	,  text_encoder=a	,  tokenizer=a	,  safety_checker=a	,  feature_extractor=self.dummy_extractor	,  )
								__UpperCamelCase      :  int       =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,  do_normalize=a		)
								__UpperCamelCase      :  Optional[int]       =   alt_pipe.to(a		)
								alt_pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  Union[str, Any]       =   "A painting of a squirrel eating a burger"
								__UpperCamelCase      :  Optional[int]       =   torch.Generator(device=a		).manual_seed(0		)
								__UpperCamelCase      :  Any       =   alt_pipe(
								    [prompt]	,  generator=a	,  guidance_scale=6.0	,  num_inference_steps=2	,  output_type="np"	,  image=a	,  )

								__UpperCamelCase      :  int       =   output.images

								__UpperCamelCase      :  Dict       =   torch.Generator(device=a		).manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   alt_pipe(
								    [prompt]	,  generator=a	,  guidance_scale=6.0	,  num_inference_steps=2	,  output_type="np"	,  image=a	,  return_dict=a	,  )[0]

								__UpperCamelCase      :  Dict       =   image[0, -3:, -3:, -1]
								__UpperCamelCase      :  Optional[Any]       =   image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCamelCase      :  str       =   np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3







	@unittest.skipIf(torch_device != "cuda"	,  "This test requires a GPU"		)
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								__UpperCamelCase      :  int       =   self.dummy_cond_unet
								__UpperCamelCase      :  str       =   PNDMScheduler(skip_prk_steps=a		)
								__UpperCamelCase      :  List[Any]       =   self.dummy_vae
								__UpperCamelCase      :  Optional[Any]       =   self.dummy_text_encoder
								__UpperCamelCase      :  Optional[Any]       =   XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"		)
								__UpperCamelCase      :  int       =   7_7

								__UpperCamelCase      :  List[str]       =   self.dummy_image.to(a		)

								# put models in fp16
								__UpperCamelCase      :  int       =   unet.half()
								__UpperCamelCase      :  List[Any]       =   vae.half()
								__UpperCamelCase      :  List[Any]       =   bert.half()

								# make sure here that pndm scheduler skips prk
								__UpperCamelCase      :  str       =   AltDiffusionImgaImgPipeline(
								    unet=a	,  scheduler=a	,  vae=a	,  text_encoder=a	,  tokenizer=a	,  safety_checker=a	,  feature_extractor=self.dummy_extractor	,  )
								__UpperCamelCase      :  int       =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,  do_normalize=a		)
								__UpperCamelCase      :  Any       =   alt_pipe.to(a		)
								alt_pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  Dict       =   "A painting of a squirrel eating a burger"
								__UpperCamelCase      :  Any       =   torch.manual_seed(0		)
								__UpperCamelCase      :  Tuple       =   alt_pipe(
								    [prompt]	,  generator=a	,  num_inference_steps=2	,  output_type="np"	,  image=a	,  ).images

								assert image.shape == (1, 3_2, 3_2, 3)







	@unittest.skipIf(torch_device != "cuda"	,  "This test requires a GPU"		)
	def   _lowerCamelCase							(   self						:Dict		)						->					Optional[Any]:
								__UpperCamelCase      :  List[str]       =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/img2img/sketch-mountains-input.jpg"		)
								# resize to resolution that is divisible by 8 but not 16 or 32
								__UpperCamelCase      :  Any       =   init_image.resize((7_6_0, 5_0_4)		)

								__UpperCamelCase      :  List[Any]       =   "BAAI/AltDiffusion"
								__UpperCamelCase      :  List[Any]       =   AltDiffusionImgaImgPipeline.from_pretrained(
								    a	,  safety_checker=a	,  )
								pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)
								pipe.enable_attention_slicing()

								__UpperCamelCase      :  List[str]       =   "A fantasy landscape, trending on artstation"

								__UpperCamelCase      :  List[str]       =   torch.manual_seed(0		)
								__UpperCamelCase      :  Tuple       =   pipe(
								    prompt=a	,  image=a	,  strength=0.75	,  guidance_scale=7.5	,  generator=a	,  output_type="np"	,  )
								__UpperCamelCase      :  Tuple       =   output.images[0]

								__UpperCamelCase      :  Tuple       =   image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 7_6_0, 3)
								__UpperCamelCase      :  int       =   np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2




@slow
@require_torch_gpu
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Optional[int]		)						->					List[str]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Tuple       =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/img2img/sketch-mountains-input.jpg"		)
								__UpperCamelCase      :  str       =   init_image.resize((7_6_8, 5_1_2)		)
								__UpperCamelCase      :  Union[str, Any]       =   load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"		)

								__UpperCamelCase      :  List[Any]       =   "BAAI/AltDiffusion"
								__UpperCamelCase      :  str       =   AltDiffusionImgaImgPipeline.from_pretrained(
								    a	,  safety_checker=a	,  )
								pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)
								pipe.enable_attention_slicing()

								__UpperCamelCase      :  int       =   "A fantasy landscape, trending on artstation"

								__UpperCamelCase      :  Optional[int]       =   torch.manual_seed(0		)
								__UpperCamelCase      :  Any       =   pipe(
								    prompt=a	,  image=a	,  strength=0.75	,  guidance_scale=7.5	,  generator=a	,  output_type="np"	,  )
								__UpperCamelCase      :  Tuple       =   output.images[0]

								assert image.shape == (5_1_2, 7_6_8, 3)
								# img2img is flaky across GPUs even in fp32, so using MAE here
								assert np.abs(expected_image - image		).max() < 1E-2

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    :   Tuple						=			{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   int						=			[
		    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LukeForEntityClassification',
		    'LukeForEntityPairClassification',
		    'LukeForEntitySpanClassification',
		    'LukeForMultipleChoice',
		    'LukeForQuestionAnswering',
		    'LukeForSequenceClassification',
		    'LukeForTokenClassification',
		    'LukeForMaskedLM',
		    'LukeModel',
		    'LukePreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		lowercase    :   Any						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)